Hamiltonian replica exchange augmented with diffusion-based generative models and importance sampling to assess biomolecular conformational basins and barriers

TL;DR

This paper presents a novel enhanced sampling framework combining Hamiltonian replica exchange, diffusion-based generative models, and importance sampling to efficiently explore biomolecular free-energy landscapes, especially in high-barrier regions.

Contribution

The study adapts diffusion models to REST2, introduces an iterative scheme with importance sampling, and demonstrates improved efficiency and accuracy in sampling biomolecular conformations.

Findings

DDPM-refined REST2 matches TREM accuracy with fewer replicas

The method uncovers high-barrier transitions in enzyme PTP1B

Approach reduces computational overhead compared to traditional methods

Abstract

Enhanced sampling techniques are essential for exploring biomolecular conformational dynamics that occur on timescales inaccessible to conventional molecular dynamics (MD) simulations. This study introduces a framework that combines Hamiltonian replica exchange with solute tempering (REST2) with denoising diffusion probabilistic models (DDPMs) and importance sampling to enhance the mapping of conformational free-energy landscapes. Building on previous applications of DDPMs to temperature replica exchange (TREM), we propose two key improvements. First, we adapt the method to REST2 by treating potential energy as a fluctuating variable. This adaptation allows for more efficient sampling in large biomolecular systems. Second, to further improve resolution in high-barrier regions, we develop an iterative scheme combining replica exchange, DDPM, and importance sampling along known collective variables. Benchmarking on the mini-protein CLN025 demonstrates that DDPM-refined REST2 achieves comparable accuracy to TREM while requiring fewer replicas. Application to the enzyme PTP1B reveals a loop transition pathway consistent with prior complex biased simulations, showcasing the approach's ability to uncover high-barrier transitions with minimal computational overhead with respect to conventional replica exchange approaches. Overall, this hybrid strategy enables more efficient exploration of free-energy landscapes, expanding the utility of generative models in enhanced sampling simulations.